class_docstr: Results from applying the Kalman smoother and/or filter to a state space
  model.
  
  Parameters
  ----------
  model : Representation
      A Statespace representation
  
  Attributes
  ----------
  nobs : int
      Number of observations.
  k_endog : int
      The dimension of the observation series.
  k_states : int
      The dimension of the unobserved state process.
  k_posdef : int
      The dimension of a guaranteed positive definite covariance matrix
      describing the shocks in the measurement equation.
  dtype : dtype
      Datatype of representation matrices
  prefix : str
      BLAS prefix of representation matrices
  shapes : dictionary of name:tuple
      A dictionary recording the shapes of each of the representation
      matrices as tuples.
  endog : ndarray
      The observation vector.
  design : ndarray
      The design matrix, :math:`Z`.
  obs_intercept : ndarray
      The intercept for the observation equation, :math:`d`.
  obs_cov : ndarray
      The covariance matrix for the observation equation :math:`H`.
  transition : ndarray
      The transition matrix, :math:`T`.
  state_intercept : ndarray
      The intercept for the transition equation, :math:`c`.
  selection : ndarray
      The selection matrix, :math:`R`.
  state_cov : ndarray
      The covariance matrix for the state equation :math:`Q`.
  missing : array of bool
      An array of the same size as `endog`, filled with boolean values that
      are True if the corresponding entry in `endog` is NaN and False
      otherwise.
  nmissing : array of int
      An array of size `nobs`, where the ith entry is the number (between 0
      and k_endog) of NaNs in the ith row of the `endog` array.
  time_invariant : bool
      Whether or not the representation matrices are time-invariant
  initialization : str
      Kalman filter initialization method.
  initial_state : array_like
      The state vector used to initialize the Kalamn filter.
  initial_state_cov : array_like
      The state covariance matrix used to initialize the Kalamn filter.
  filter_method : int
      Bitmask representing the Kalman filtering method
  inversion_method : int
      Bitmask representing the method used to invert the forecast error
      covariance matrix.
  stability_method : int
      Bitmask representing the methods used to promote numerical stability in
      the Kalman filter recursions.
  conserve_memory : int
      Bitmask representing the selected memory conservation method.
  tolerance : float
      The tolerance at which the Kalman filter determines convergence to
      steady-state.
  loglikelihood_burn : int
      The number of initial periods during which the loglikelihood is not
      recorded.
  converged : bool
      Whether or not the Kalman filter converged.
  period_converged : int
      The time period in which the Kalman filter converged.
  filtered_state : ndarray
      The filtered state vector at each time period.
  filtered_state_cov : ndarray
      The filtered state covariance matrix at each time period.
  predicted_state : ndarray
      The predicted state vector at each time period.
  predicted_state_cov : ndarray
      The predicted state covariance matrix at each time period.
  kalman_gain : ndarray
      The Kalman gain at each time period.
  forecasts : ndarray
      The one-step-ahead forecasts of observations at each time period.
  forecasts_error : ndarray
      The forecast errors at each time period.
  forecasts_error_cov : ndarray
      The forecast error covariance matrices at each time period.
  loglikelihood : ndarray
      The loglikelihood values at each time period.
  collapsed_forecasts : ndarray
      If filtering using collapsed observations, stores the one-step-ahead
      forecasts of collapsed observations at each time period.
  collapsed_forecasts_error : ndarray
      If filtering using collapsed observations, stores the one-step-ahead
      forecast errors of collapsed observations at each time period.
  collapsed_forecasts_error_cov : ndarray
      If filtering using collapsed observations, stores the one-step-ahead
      forecast error covariance matrices of collapsed observations at each
      time period.
  standardized_forecast_error : ndarray
      The standardized forecast errors
  smoother_output : int
      Bitmask representing the generated Kalman smoothing output
  scaled_smoothed_estimator : ndarray
      The scaled smoothed estimator at each time period.
  scaled_smoothed_estimator_cov : ndarray
      The scaled smoothed estimator covariance matrices at each time period.
  smoothing_error : ndarray
      The smoothing error covariance matrices at each time period.
  smoothed_state : ndarray
      The smoothed state at each time period.
  smoothed_state_cov : ndarray
      The smoothed state covariance matrices at each time period.
  smoothed_state_autocov : ndarray
      The smoothed state lago-one autocovariance matrices at each time
      period: :math:`Cov(\alpha_{t+1}, \alpha_t)`.
  smoothed_measurement_disturbance : ndarray
      The smoothed measurement at each time period.
  smoothed_state_disturbance : ndarray
      The smoothed state at each time period.
  smoothed_measurement_disturbance_cov : ndarray
      The smoothed measurement disturbance covariance matrices at each time
      period.
  smoothed_state_disturbance_cov : ndarray
      The smoothed state disturbance covariance matrices at each time period.
code: def smoothed_forecasts_error_cov(self):
      return self._get_smoothed_forecasts()[2]
  
imports: import numpy
  from types import SimpleNamespace
  from statsmodels.tsa.statespace.representation import OptionWrapper
  from statsmodels.tsa.statespace.kalman_filter import KalmanFilter, FilterResults
  from statsmodels.tsa.statespace.tools import reorder_missing_matrix, reorder_missing_vector, copy_index_matrix
  from statsmodels.tsa.statespace import tools, initialization
label_desc: Use image node_id 10 for calling the SmootherResults obj's underlying member method code with example usage: obj.smoothed_forecasts_error_cov() and returns: self